In recent times, ransomware has become the most significant cyber-attack targeting individuals, enterprises, healthcare industries, and the Internet of Things (IoT). Existing security systems like Intrusion Detection and Prevention System (IDPS) and Anti-virus (AV) as a single monitoring agent is complicated and time-consuming, thus fails in ransomware detection. A robust Intrusion Detection Honeypot (IDH) is proposed to address the issues mentioned above. IDH consists of i) Honeyfolder, ii) Audit Watch, and iii) Complex Event Processing (CEP). Honeyfolder is a decoy folder modeled using Social Leopard Algorithm (SoLA), especially for getting attacked and acting as an early warning system to alert the user during the suspicious file activities. AuditWatch is an Entropy module that verifies the entropy of the files and folders. CEP engine is used to aggregate data from different security systems to confirm the ransomware behavior, attack pattern, and promptly respond to them. The proposed IDH is experimentally tested in a secured testbed using more than 20 variants of recent ransomware of all types. The experimental result confirms that the proposed IDH significantly improves the ransomware detection time, rate, and accuracy compared with the existing state of the art ransomware detection model.

Wearables are getting large acceptance in the continuous monitoring of health status and physiological data. Medical devices and their connectivity through Internet along with the electronics health record (EHR) and AI analytics making smart healthcare possible. Internet-of-Medical-Things (IoMT)-end devices like wearables and implantables are key for smart healthcare. Smart garment is a specific wearable which can be used for smart healthcare. This paper presents the design and development of a smart garment called MyWear that continuously monitors and collects physiological data. It can analyze muscle activity, stress levels, and heart rate variations and send all the data to the cloud. Through abnormal variations in vitals, it can also predict the risk of heart failure and with the in-built alert system, it can notify the associated medical officials if necessary. We also propose a deep neural network model that classifies heartbeat data into abnormalities with 96.9% accuracy and 97.3% precision.

Yoga has been a great form of physical activity and one of the promising applications in personal health care. Several studies prove that yoga is used as one of the physical treatments for cancer, musculoskeletal disorder, depression, Parkinson’s disease, and respiratory heart diseases. In yoga, the body should be mechanically aligned with some effort on the muscles, ligaments, and joints for optimal posture. Postural-based yoga increases flexibility, energy, overall brain activity and reduces stress, blood pressure, and back pain. Body Postural Alignment is a very important aspect while performing yogic asanas. Many yogic asanas including uttanasana, kurmasana, ustrasana, and dhanurasana, require bending forward or backward, and if the asanas are performed incorrectly, strain in the joints, ligaments, and backbone can result, which can cause problems with the hip joints. Hence it is vital to monitor the correct yoga poses while performing different asanas. Yoga posture prediction and automatic movement analysis are now possible because of advancements in computer vision algorithms and sensors. This research investigates a thorough analysis of yoga posture identification systems using computer vision, machine learning, and deep learning techniques.

Discovering malicious packets amid a cloud of normal activity, whether you use an IDS or gather and analyze machine and device log files on company infrastructure, may be challenging and time consuming. The vulnerability landscape is rapidly evolving, and it will only become worse as more and more developing technologies, such as IoT, Industrial Automation, CPS, Digital Twins, etc are digitally connected. A honey trap aids in identifying malicious packets easily as, after a few rapid calibrations to eliminate false positives. Besides analyzing and reporting particular invasion patterns or toolkits exploited, it also assists in preventing access to actual devices by simulating the genuine systems and applications functioning in the network thus delaying as well as baffling the invader. In order to analyze and evaluate the hackers’ behavior, an ensemble of research honeypot detectors has been deployed in our work. This paper delivers a robust outline of the deployment of containerized honeypot deployment, as a direct consequence, these are portable, durable, and simple to deploy and administer. The instrumented approach was monitored and generated countless data points on which significant judgments about the malevolent users’ activities and purpose could be inferred.